Digital video processing is currently used in a variety of applications. Digital video processing includes video acquisition, restoration, encoding, decoding, object recognition, and data filtering and enhancement. The video that is processed during digital video processing includes, but is not limited to, motion pictures and images. During processing, artifacts may be introduced in the video, and it may become distorted. The artifacts cause incorrect visualization of the video. The artifacts include, but are not limited to, an interlace artifact, a blur artifact, an aliasing artifact, a noise artifact, a ringing artifact, and a blockiness artifact. The blur artifact is distinctly visualized in the video, since the blur artifact affects a series of frames included in the video.
The frames of the video are affected by the blur artifact because of various factors. Generally, a video acquisition device is unable to correctly focus the objects to be captured; therefore, the video may become affected by the blur artifact and the noise artifact. Further, a high relative motion between the video acquisition device and the objects to be captured may also cause the blur artifact. As a result of the high relative motion, focusing of the objects by the video acquisition device becomes difficult. Furthermore, there are various additional factors which contribute to the video being affected by the blur artifact. During encoding of the video, some frequency components of the video are lost due to compression algorithms. Therefore, the video is not fully reconstructed while decoding. As a result, the video is affected by the blur artifact. The video can also be affected by the blur artifact during processing of the video. During processing, the high frequency components of the video are passed through low-pass filters, which cause spreading of the edges of the captured objects. The edge spread is then visualized as the blur artifact by the human eye.
The presence of the blur artifact leads to an incorrect perception of the video by the human eye. Therefore, techniques for calculating and subsequently removing the blur artifact from the video are desirable. A number of techniques exist for removing the blur artifact. However, due to inaccurate calculation of the blur artifact, the existing techniques are unable to efficiently remove the blur artifact. Therefore, to efficiently remove the blur artifact, techniques for accurately calculating the blur artifact are desirable.
An existing technique for calculating the blur artifact detects edges in the video and calculates the blur artifact on the basis of the detected edges. This technique is generally applied to the objects in the video which have a high depth of field. However, the incorrect visualization of the video, because of the blur artifact, predominantly occurs in objects with low depth of field in the video. Therefore, the blur artifact calculated by using this technique can be inaccurate. Further, this technique does not calculate a perceived quality of the calculated blur artifact, since the technique does not calculate blurred frames of the video. Moreover, duration of the blur artifact is not calculated.
Another technique for calculating the blur artifact classifies the image into character blocks and background blocks. An energy ratio of the character blocks is then analyzed to calculate the blur artifact in the image. However, this technique does not identify focused areas to calculate the blur artifact in an image. The technique is also computationally intensive and therefore slow.
Still another technique for calculating the blur artifact in the video uses a full reference method. The captured video is compressed during encoding. For the calculation of the blur artifact, the compressed video is decompressed and the pixels of the frames of the compressed video are compared with the pixels of the frames of the captured video. The pixels are compared on the basis of various techniques, such as Peak Signal to Noise Ratio (PSNR), and sum of absolute differences of pixels. Yet another traditional technique for calculating the blur artifact in the video uses a reduced reference method. In the reduced reference method, the features of the pixels are calculated by using various techniques such as a mean of the pixels, or a variance of the pixels. Thereafter, the features of the pixels in the decompressed video and the features of the pixels in the captured video are compared. Subsequently, edges of the objects in the frames of the captured video are detected by an edge detection filter. Still another traditional technique calculates the blur artifact in the video by using a no-reference method. The captured video is compressed and the blur artifact is calculated in the compressed video. The aforementioned techniques calculate the blur artifact in frames of a video. However, no focused area is identified in the frames. As a result, the objects in the video which are not present in the focused area are also considered while calculating the blur artifact. The objects that are not present in the focused area of the frames are less prone than the objects in the focused area to be perceived as being affected by the blur artifact. Therefore, the blur artifact calculated by the aforesaid techniques tends to be inaccurate.
Yet another technique for calculating the blur artifact uses the wavelet method. However, this technique does not identify focused areas before calculating the blur artifact. Therefore, the technique does not calculate the blur artifact for the objects with low depth of field in the video. Still another technique for calculating the blur artifact calculates the blur artifact in a focused area of the image using the Discrete Cosine Transform. Both the aforesaid techniques are computationally intensive, and therefore slow.
Yet another technique for calculating the blur artifact senses the blur artifact in the video by using sensors that are included in the video acquisition device. Subsequently, the focusing of the objects to be captured by the video acquisition device is altered. The altering of the focus reduces the blur artifact in the captured video. However, the perception quality of the calculated blur artifact is not calculated by this technique.
Therefore, there exists a need for a method and a system for calculating a blur artifact in a video. The method and the system should preferably be capable of identifying a focused area in the video, and calculating an accurate value of the blur artifact for the objects in the focused area. Moreover, the method and the system should preferably be capable of calculating the blur artifact for the objects with low depth of field in the video. Furthermore, the method and the system should preferably also be capable of calculating the perceived quality and duration of the blur artifact in the video.